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10.1371/journal.pone.0310034
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Feeling tired versus feeling relaxed: Two faces of low physiological arousal
Feeling tired versus feeling relaxed
https://orcid.org/0009-0006-5617-1249
Steghaus Sarah Conceptualization Data curation Formal analysis Investigation Methodology Resources Visualization Writing – original draft Writing – review & editing *
https://orcid.org/0000-0003-1621-4911
Poth Christian H. Conceptualization Project administration Supervision Writing – review & editing
Neuro-cognitive Psychology and Center of Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany
Steinborn Michael B. Editor
Julius-Maximilians-Universität Würzburg: Julius-Maximilians-Universitat Wurzburg, GERMANY
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: sarah.steghaus@uni-bielefeld.de
9 9 2024
2024
19 9 e03100346 6 2024
23 8 2024
© 2024 Steghaus, Poth
2024
Steghaus, Poth
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Human well-being and functioning depend on two fundamental mental states: Relaxation and sleepiness. Relaxation and sleepiness are both assumed to be states of low physiological arousal and negatively correlated. However, it is still unclear how consistent this negative relationship is across different settings and whether it changes before and after an intervention. Here we investigated this intricate relationship between subjective momentary sleepiness and relaxation states by meta-analytically analyzing several data sets from studies using the Relaxation State Questionnaire. We discovered that subjective sleepiness and relaxation were in fact anti-correlated pre-intervention. This anti-correlation provides a quantitative dissociation between sleepiness and relaxation. Thus, even though sleepiness and relaxation both implicate a low arousal level, the two mental states are subjectively experienced in a qualitatively different fashion, and thus reflect distinct underlying constructs. For the post-intervention relationship, this negative correlation could not be consistently found. This indicates that there are aspects of the experimental setting or intervention that introduce changes in the dynamics of the relationship of the two constructs.

The author(s) received no specific funding for this work. Data AvailabilityAll data and the data analysis code are made available on the OSF (https://osf.io/34bsa/?view_only=391e8b016365446ba8b07b316cbd1067).
Data Availability

All data and the data analysis code are made available on the OSF (https://osf.io/34bsa/?view_only=391e8b016365446ba8b07b316cbd1067).
==== Body
pmcIntroduction

In our fast-paced world, the importance of relaxation has gained attention, as stress and stress related problems are on the rise. According to an APA-report, 76% of adults have experienced “health impacts due to stress in the prior month” [1]. With stress becoming a prevalent concern impacting mental and physical well-being, the significance of relaxation and relaxation practices to counteract these problems, cannot be overstated. Even though there have been numerous studies on the positive effect of relaxation and relaxation exercises [e.g., 2–4], it is difficult to find a definition of the construct relaxation. Relaxation has many variations and contains physical, neurological and psychological components [5, chapter 27]. Usually, relaxation implies a reduction of arousal, somewhat opposed to the stress related fight-or-flight response [6, 7, chapter 3]. Understanding relaxation in the current context not only addresses the immediate challenges of stress but also aligns with broader efforts to enhance overall quality of life in a rapidly evolving society [8].

One equally important and related construct is sleepiness. Similar to relaxation, sleepiness encompasses different facets, but overall it can be defined as an increased propensity to fall asleep [9, 10]. Sleepiness is also correlated with both stress and psychological well-being [11, 12]. Experiencing stress in everyday life not only directly impacts individuals’ health, but also results in poorer sleep quality and increased daytime sleepiness [13]. Daytime sleepiness directly impacts well-being and cognitive performance and may also lead to accidents and other problems [9, 14]. Thus, delving into the multifaceted implications of both relaxation and sleepiness may be crucial for fostering individual well-being and resilience in the face of modern stressors.

Intriguingly, sleepiness is, like relaxation, connected to a reduced arousal level. Arousal is referred to as a “generalized bodily activation” [15]. It could already be demonstrated in several experiments and settings that high states of arousal (e.g., externally elicited by alerting signals) can influence perception, cognition, and action [e.g., 16–18]. Low arousal levels are indicated by less activation; therefore, it is quite intuitive that individuals in a relaxed state, but also in a sleepy state, have low arousal levels. However, how these low arousal levels influence cognition has not yet received as much attention as the high arousal mechanisms.

Sleepiness and relaxation

The basis for understanding the intriguing inverse relationship between sleepiness and relaxation can be traced back to works in mood and arousal research. Early works in this field proposed that relaxation and sleepiness are on the same spectrum of low arousal [for an overview see 19, chapter 3]. Sleepiness or tiredness was considered an extreme form of low arousal or low activation [20, 21], even lower than relaxation. However, one can easily imagine cases where this is not true. People often experience extreme tiredness without being relaxed at all, e.g., exhaustion. Also, it is perfectly plausible to be relaxed and not sleepy but rather mindful, calm, and concentrated. In line with this subjective experience, are works that propose distinct aspects of low arousal and emphasize the multifaceted nature of it. Thayer (1989) proposed two distinct systems of arousal: energetic arousal (EA) and tense arousal (TA). EA describes the subjective experience of wakefulness and alertness, with a decline leading to tiredness and diminished motivation for action. In contrast, TA is associated with feelings of tension, anxiety, stress, and fear. Interestingly, this system can also lead to mobilized action despite the presence of negative emotions. A decline in TA, however, is aligned with relaxation and a reduction in the physiological markers of stress. Furthermore, the two dimensions can interact in different ways to produce various combinations of mental states [19]. EA and TA may be categorized into high (+) and low (-) states, allowing four different combinations of EA and TA. Table 1 shows a fourfold table with all possible combinations, giving everyday situations where they may occur.

10.1371/journal.pone.0310034.t001 Table 1 Fourfold table with arousal combinations.

	TA+	TA-	
EA+	Challenge or high-stakes scenario	Enjoyment or pleasure	
Individuals are very engaged, involved and energetic, but also under a certain degree of pressure, urgency or stress	Individuals are energetic and positive, but relaxed and calm at the same time	
Examples: Public speaking, important exam, competitive sports	Examples: Non-competitive sports, playing games, pursuing a hobby	
EA-	Anxiousness	Passivity	
Individuals are tense, nervous and/ or worrying, but at the same time without energy	Individuals are experiencing a lack of energy and enthusiasm while also experience no tension	
Examples: Threatening or overwhelming situations	Examples: Lack of motivation, apathy, indifference	
TA+ = high tense arousal. TA- = low tense arousal. EA+ = high energetic arousal. EA- = low energetic arousal.

Both systems of arousal—energetic and tense—are rooted in intricate biochemical processes within the body. The intricate interplay of neurotransmitter systems, particularly the adrenergic pathway, has been implicated in governing both energetic and tense arousal. The autonomic nervous system, with its sympathetic and parasympathetic branches, also plays an essential role in shaping the physiological manifestations of these states [19].

A study by Huelsman et al. (1998) served as an early precursor, highlighting the negative correlation (r = -.46) between sleepiness and relaxation. Interestingly, the study considers relaxation and sleepiness to be traits and, while a factor analysis marked them as distinct factors, textual they are labeled both as “low affectivity”. Later work by Schimmack and Grob [22] pointed out that the two forms of arousal (energetic and tense arousal) are indeed two independent factors and not just facets of one main arousal dimension.

This negative correlation between sleepiness and relaxation was also found in the construction of the Relaxation State Questionnaire [RSQ; 23]. The RSQ is a novel tool assessing subjective relaxation and its short-term changes. Measuring short-term effects and changes in relaxation has long been neglected, since the focus of the research was mainly on long-term relaxation and trait-like conceptions rather than situation-dependent state. However, to be able to account for changes introduced by e.g., experimental interventions, a measurement sensitive to short-term changes is essential. The RSQ solves this issue and is able to differentiate relaxation into 3 subscales and one sleepiness scale. All scales have been established by two independent factor analyses. Relaxation aspects covered by the RSQ are divided into the categories: Muscle Relaxation (focusing on changes e.g., evoked by Progressive Muscle Relaxation exercises [24, 25]), Cardiovascular Relaxation (capturing individually observable body-related changes in breathing and heart rate) and General Relaxation (meaning overall relaxation items with a high face-validity, such as answering whether or not one feels relaxed) [23]. The two independent factorial analyses also revealed a negative correlation between sleepiness and the three highly correlated factors of relaxation (Muscle Relaxation, Cardiovascular Relaxation, and General Relaxation). Therefore, the RSQ provides an economic and easily applicable tool to measure both relaxation and sleepiness at the same time.

Research questions and objectives

Further understanding the complex relationship between sleepiness and the facets of relaxation can impact both the theoretical understanding of the constructs and have a variety of practical implications, e.g., concerning stress management or well-being. While state questionnaires (and other measures) are frequently used in research, especially in experimental settings, the underlying concepts are often not clearly distinguished. This may lead to confusion between similar-sounding terms with different meanings (like being tired vs. being relaxed, for another example see [26]). In therapeutic settings for example, a gain in relaxation after an intervention would ideally be accompanied by a decrease in sleepiness during the day to allow patients to feel both calm, but still present, attentive, and observant (corresponding to Enjoyment from Table 1). Thus, it is vital to be able to understand how these two states combine, interact, and affect e.g., performance, well-being, and other outcomes in different settings. Especially since overarching states (such as relaxation and sleepiness) may very well be context specific as it was already demonstrated with other constructs [26, 27]. To explore this, the present study analyses a set of 11 independent studies that all used the RSQ in various settings. Meta-analytic methods will then be able to reveal overarching trends for the relationship between sleepiness and relaxation over all studies.

Pre-post-data

The RSQ not only allows an efficient measurement of relaxation and sleepiness, but due to its briefness and its state-conception of the constructs, it gives the opportunity for measuring the changes in the states and their interaction over time, e.g., over the course of an experiment. Measuring states before and after an intervention is useful and insightful for several purposes: Measuring a state before a task or intervention allows researchers not only to get a baseline measurement of the participants mental state, but also to predict outcomes based on these measurements. Accordingly, post-measurements allow conclusions to be drawn e.g., about the effect or effectiveness of an intervention. Looking closer at the dynamics and interactions of mental states before and after interventions may give insights into specific changes of feelings or perceptions of individuals due to a task or intervention. This provides researchers with the opportunity to closer examine the contextual dynamics e.g., of the relationship between sleepiness and relaxation overall.

It could already be shown that even traits (which per definition are supposed to be stable) may be sensitive to change [e.g., 28, 29]. The closer examination of states (which per definition are sensitive to change) and their dynamics and relationships may thus be insightful and enlightening. Therefore, here we also ask, how the intriguing negative relationship between sleepiness and relaxation behaves before and after interventions (that elicit a change in the subjective relaxed state) for each of the subscales of relaxation.

Methods

Relaxation State Questionnaire (RSQ)

The Relaxation State Questionnaire [RSQ; 23] is a 10-item questionnaire designed to assess subjective relaxation and its short term changes. Each item is written as a statement to which participants are asked to rate their agreement to on a 5-point-likert-scale (1 = do not agree at all; 5 = totally agree). The items can be divided into 4 scales: The Cardiovascular Scale, the Muscle Scale, the General Relaxation Scale, and the Sleepiness Scale. As the first three scales measure parts of relaxation, they are highly correlated (r = .41, .61, and .80, respectively). Interestingly, two independent factorial analyses of the scales showed that they are also negatively correlated with the Sleepiness Scale. It was proposed that the Sleepiness Scale could therefore pose as a manipulation check e.g., an indicator for answering tendencies from participants. However, this also grants the opportunity to investigate the negative relationship between different aspects of relaxation and sleepiness and the alteration of that correlation after interventions. The RSQ has been factorially validated, has a high face validity and good reliability (α = 0.86), item parameters, and construct validity. Because of its efficiency it can easily be included in different studies and settings.

Set of studies

A total of 11 experiments were included in the meta-analyses. Data collection took place over a period of three years (starting in 2021, ending in April 2023). All participants gave written informed consent before the studies and no minors were included in any of the experiments. The studies conformed to the ethical guidelines of the German Psychological Association (DGPs) and were approved by the ethics committee of Bielefeld University.

Experiments showed a great variation of settings, design, and intervention (see Table 2 for details on each experiment). Since the RSQ was mostly used in pre-post-designs, a total of 6 (3 x 2) different analyses were performed: For each of the three relaxation scales of the RSQ, two different analyses were computed. One for the pre-measurements of the experiments and one with the post-measurements. For the pre-measurements the whole sample was used for each study. For the post-measurements, the samples were divided into different groups (see Table 2), if the participants received different types of interventions. Therefore, the meta-analyses for the post-measurements contained 20 data subsets whereas the analyses for the pre-measurements only contained 10.

10.1371/journal.pone.0310034.t002 Table 2 Overview of studies used for the meta-analyses.

	Sample (x Sessions)	Design	Setting	Description	
Study 1	6 x 10	within	lab	1-hour eye tracking experiment with alertness cues	
Study 2	99	survey	online	Survey after watching 45min of online Video-streaming (only in post-meta-analyses)	
Study 3	95	mixed with 3 groups	lab	Laboratory experiment with breathing exercises followed by the TMT	
Group A	30	Control condition (1 min waiting)	
Group B	39	Short relaxation condition (5 min breathing exercise)	
Group C	26	Longer relaxation condition (11 min breathing exercise)	
Study 4	6 x 5	within	lab	Eye tracking experiment with Flanker Task	
Study 5	61	within	online	30 min PMR intervention and relaxation questionnaires	
Study 6	144	within	online	10 min PMR intervention	
Study 7	109	mixed with 2 groups	online	Stressful learning vs. relaxation experiment with relaxation and tension questionnaires	
Group A	57	Relaxation condition (15 min PMR audio instruction)	
Group B	52	Stressful condition (Vocabulary learning experiment under time pressure)	
Study 8	5 x 10	within	lab	Muscle tensing and relaxing exercises and TVA trials	
Study 9	7 x 5	within	lab	PMR and antisaccade task	
t1	7 x 5	First Post-Measurement after 160 trials of an antisaccade task with eye tracking	
t2	7 x 5	Second Post-Measurement after 15 min of PMR audio instruction	
Study 10	49	mixed with 3 groups	online	Online experiment with 3 group and pre-post relaxation measurements	
Group A	14	Relaxing condition (15min PMR audio instruction)	
Group B	13	Neutral condition (picture description task)	
Group C	22	Stressful condition (Color Word Stroop under time pressure and impossible number of trials)	
Study 11	148	mixed with 3 groups		PMR intervention and Mackworth-clock-task with different levels	
Post	147	First measurement after PMR intervention, before Mackworth-clock-task	
Group A	48	Mackworth-clock-task with 2 positions skipped	
Group B	59	Mackworth-clock-task with 1 position skipped	
Group C	39	Control (no Mackworth-clock-task)	
Sample denotes N or n in the subgroup, if applicable with number of sessions for each person. Within indicates a within-subjects design with repeated measures. Setting is categorized into laboratory based (lab) settings and online settings. TMT = Trail Making Test [30]. PMR = Progressive Muscle Relaxation [25]. TVA = Theory of visual attention [31]. t1 and t2 denote the different measurement time points.

Data analysis

The meta-analyses were performed with the metafor package [32]. As outcome measures, Fishers z-transformed correlation coefficient was used, because the set of studies contained studies with a rather small sample size [33, but see also 34]. Each correlation was against the Sleepiness Scale of the RSQ. Then, random effects models were fitted with maximum likelihood estimation [32] and forest plots were computed. Also, to visually control for biases in our data, funnel plots were plotted [35].

Results

Correlations pre-intervention

Muscle scale

The pre-intervention data for the Muscle Scale showed a medium amount of heterogeneity (I2 = 58.79%). The estimated correlation is β = -.26, p < .001, 95% CI [-.38, -.14]. Fig 1 shows the forest plot with the corresponding correlation from each study.

10.1371/journal.pone.0310034.g001 Fig 1 Forest plot for the data of the muscle scale in the pre-intervention Measurements.

Fisher’s z transformed correlation with 95-% Confidence intervals are reported for each study. Bigger black squares indicate a higher N. Designations of datasets are corresponding to Table 2.

General relaxation scale

For the General Relaxation Scale there was also a medium amount of heterogeneity in data (I2 = 46.30%). The estimated correlation was similar to the one found for the Muscle Scale: β = -.27, p < .001, 95% CI [-.38, -.17] (see also Fig 2).

10.1371/journal.pone.0310034.g002 Fig 2 Forest plot for the data of the general relaxation scale in the pre-intervention measurements.

Fisher’s z transformed correlation with 95-% Confidence intervals are reported for each study. Bigger black squares indicate a higher N. Designations of datasets are corresponding to Table 2.

Cardiovascular scale

Heterogeneity for the Cardiovascular Scale was not as high as for the other scales (I2 = 35.85%). Also, the estimated correlation with sleepiness was lower for this scale (see also Fig 3), β = -.16, p < .001, 95% CI [-.25, -.07].

10.1371/journal.pone.0310034.g003 Fig 3 Forest plot for the data of the cardiovascular scale in the pre-intervention measurements.

Fisher’s z transformed correlation with 95-% Confidence intervals are reported for each study. Bigger black squares indicate a higher N. Designations of datasets are corresponding to Table 2.

Correlations post-intervention

Muscle scale

For the post-intervention data, the heterogeneity of the Muscle Scale was I2 = 16.18%. Correlations for all subsets and studies can be found in Fig 4. The estimated correlation was β = -.14, p < .001, 95% CI [-.21, -.07].

10.1371/journal.pone.0310034.g004 Fig 4 Forest plot for the data of the muscle scale in the post-intervention measurements.

Fisher’s z transformed correlation with 95-% Confidence intervals are reported for each study. Bigger black squares indicate a higher N. Designations of datasets are corresponding to Table 2.

General relaxation scale

Heterogeneity of the General Relaxation Scale data after the intervention was high, I2 = 60.03%. However, the random-effects model did not reach significance (β = -.10, p = .11, 95% CI [-.21, .01] (see Fig 5).

10.1371/journal.pone.0310034.g005 Fig 5 Forest plot for the data of the general relaxation scale in the post-intervention measurements.

Fisher’s z transformed correlation with 95-% Confidence intervals are reported for each study. Bigger black squares indicate a higher N. Designations of datasets are corresponding to Table 2.

Cardiovascular scale

For the post-intervention data of the Cardiovascular Scale, there was little heterogeneity, I2 = 10.66%. Also, the estimated correlation did not reach significance: β = .01, p = .712, 95% CI [-.07, .06]. Correlations for all subsets can be found in Fig 6.

10.1371/journal.pone.0310034.g006 Fig 6 Forest plot for the data of the cardiovascular scale in the post-intervention measurements.

Fisher’s z transformed correlation with 95-% Confidence intervals are reported for each study. Bigger black squares indicate a higher N. Designations of datasets are corresponding to Table 2.

Further analyses

To control for biases with the experiments (e.g., hidden selection effects), funnel plots were computed for each random-effects model. All plots can be found in the supporting information. Also, given the broad variation of studies, some aspects of the design and setting were categorized and their influence on the correlation was investigated. The factors setting (online vs. lab-based), design (repeated-measures vs. not), and type of intervention (relaxing intervention vs. not) were analyzed using t-tests and the Bayes Factor [36]. The Bayes Factor gives information about how strongly the two hypotheses (alternative vs. null hypothesis) support the existing data and can be seen as an alternative to the p value [37, 38]. Following guidelines for categorizing Bayes Factors [BF10; 39], the resulting outcomes can be quantified as follows: BF10 of 1 = equal support for the null and the alternative hypothesis, 1 to 3 = weak evidence in favor of the alternative hypothesis, and 3 to 10 = moderate evidence in favor of the alternative hypothesis. However, except for one comparison (see Table 3) no significance difference was found for either setting, design, or type of intervention. The Bayes Factor mostly supports these findings, indicating moderate evidence only for the significant comparison. Furthermore, two more BF were just above 1, indicating weak evidence for the alternative hypothesis. Table 3 shows exemplary the data for the setting-comparisons. The other comparisons can be found in S1 and S2 Tables.

10.1371/journal.pone.0310034.t003 Table 3 Mean correlations for each scale depending on setting.

	Pre-intervention	Post-intervention	
Online (n = 5)	Lab (n = 5)	p	BF10	Online (n = 12)	Lab (n = 8)	p	BF10	
M (SD)	M (SD)	
M (SD)	M (SD)	
Muscle Scale	-.2 (.16)	-.39 (.19)	.118	1.14	-.08 (.17)	-.28 (.15)	.013	3.95	
General Relaxation Scale	-.25 (.15)	-.35 (.17)	.381	0.64	-.04 (.2)	-.16 (.33)	.369	0.58	
Cardio-vascular Scale	-.16 (.15)	-.17 (.16)	.904	0.49	.06 (.22)	-.06 (.21)	.221	0.69	
n = number of studies in this category; p values in bold indicate significance

Discussion

The relationship between relaxation and sleepiness has been in the focus of research for many decades [19]. While the idea of a general dimension of arousal or activation can be useful [40], different research suggests that there are indeed two distinct dimensions of arousal, that seem to be negatively correlated [22, 41]. To extend the existing evidence and applications, [e.g., 42–44], this paper makes use of a broad variety of datasets to further investigate the relationship between relaxation and sleepiness and their dynamics before and after interventions. Using the Relaxation State Questionnaire [RSQ; 23], an economic and reliable tool that assess both short-term subjective relaxation and sleepiness, and their relationship.

The data collected for measurements before any form of intervention, included 10 different studies including both laboratory based and online studies. For all three scales of relaxation (muscle related aspects, general relaxation, and cardiovascular related aspects) a significant negative correlation with sleepiness could be found over all data sets (r = -.26, -.27, and -.16, respectively). Notably, almost every study produced a descriptive negative correlation, besides Studies 7 and 8 for the Cardiovascular Scale. Therefore, in over 93% of instances, the negative correlation was found. It is also worth mentioning that in the process of constructing the RSQ, the Cardiovascular scale displayed less strong item parameters and factor loadings [23]. Hence, the slightly lower correlation of r = -.16 and the non-negative correlations for Studies 7 and 8 in the Cardiovascular Scale, may stem from limitations regarding the measurement, not from an actual absence of a (high) negative correlation. Overall the three analyses from the pre-intervention datasets therefore confirm findings indicating that sleepiness and relaxation are two distinct dimensions and are negatively correlated [41, 45]. Our findings suggest that this relationship is consistent over different settings and environments.

The data collected for the measurements after an intervention, included 20 data subsets with a broad variety of interventions and settings. The descriptive correlations for the three relaxation scales were r = -.14, -.10, and -.01. However, only the first correlation reached significance. All correlations are also less negative than in the pre-measurement analyses. Also, in the forest plots one can find several data sets that actually produced (significant) positive correlations (e.g., Fig 6, Dataset 10A). In total 21 datasets were descriptively positive, meaning that only 65% percent of the datasets showed a negative correlation. Again, the Cardiovascular Scale had the least negative correlation and with 11 positive and 9 negative datasets, the most mixed results. There are several possible explanations for these findings. First, it is possible that because the data, the conditions, and the subgroups used in the analyses were very diverse, too many variables and therefore too much noise was introduced into the datasets (see also discussion of the further analyses below). Secondly, on a more general note, one could also raise the question on how participants are able to obtain meta-cognitive knowledge about their own subjective relaxation and their sleepiness. It is widely known that subjective and objective measures may differ in their outcomes and are therefore often both assessed to capture the whole picture of a construct [e.g., 3, 9, 46]. While subjective measures have the advantage of giving insight in the participants personal assessment and feelings, they are also prone to biases such as demanding characteristics or tendencies of certain self-presentations. In the present studies, it is plausible that participants answered the RSQ before an intervention rather honestly, however after an intervention participants answered in the direction, they thought the experiment was headed (e.g., claiming to be more relaxed after a relaxation exercise). For the sleepiness, however, it may be a lot less clear, what the ‘right’ answer after an intervention could be. Since both sleepiness and relaxation were once thought to be one dimension and are both associated with low arousal [19], participants might have rated both categories similar. For example, after completing a relaxation exercise, they might have thought that they were supposed to be more relaxed and sleepier afterwards. This would also be in line with the original recommendation of the RSQ, to use the sleepiness scale as a manipulation check for answering tendencies, since participants may not be able to distinguish between the aspects of relaxation and sleepiness [23]. To control for effects like these, objective measurements (e.g., heart rate, blood pressure) may need to be used in future studies to gain further insight in the objective aspects of relaxation and sleepiness accompanying the subjective ratings. Thirdly, it seems plausible that the found correlation is indeed accurate and not distorted by the data or the participants answering tendencies. The difference between the pre-measurements and the post-measurements is the intervention taking place in between. Depending on the type of intervention, this may affect relaxation and sleepiness quite differently. There are some studies that indicate that certain interventions can lead to different outcomes on the two dimensions [e.g., 47]. Not only are both energetic and tense arousal prone to change differently, also their dynamic may change depending on the situation or intervention [48, 49]. One idea by Thayer is that with moderate activation conditions both tense and energetic arousal behave similarly, however when one dimension is highly activated (e.g., extreme stress or pain for tense arousal and exercise or drugs for energetic arousal), this dimension “takes over” and the other dimension does not rise [49]. It could be very interesting for future experiments to manipulate the level of tense and/or energetic arousal in different stages (low–medium–high) and measure with the RSQ both relaxation and sleepiness of the participants. Thus, a mapping of the two states and their relationship depending on the different arousal levels may be possible. Lastly, the further analyses of classification of the setting, the type of intervention, and the design of the studies did not reveal significant impacts on the found correlations. Only the difference in laboratory-based vs. online studies in the post-datasets for the Muscle Scale was significant, with laboratory-based studies yielding more negative correlations (r = -.28) that online-based studies (r = -.08). However, due to the large amount of test, one significant finding seems not surprising and should be interpreted cautiously. Nonetheless, this finding calls for further substantiation by follow-up studies, because it may suggest that the relationship between relaxation and sleepiness depends on the context, and that laboratory and online assessments might influence the relationship (e.g. by modulating how the variance of test scores is dominated by experimental and individual influences, [cf. 50, 51]).

Another possible explanation for these findings regards the broad variety of studies, conditions, and datasets. While it was the goal to obtain different datasets similar to ‘traditional’ meta-analyses, this also comes with the cost that the number of completely different settings and designs generates a lot of noise that may pollute the data. Classifications made for testing the different settings and types of interventions for example were hence very rough. Relaxation exercises e.g. contained both breathing exercises of different length and progressive muscle relaxation exercises [24, 25] of different length and quality. It has been shown in several studies that different relaxation exercises produce different effects and outcomes as they have distinct principles of operation [e.g., 52, 53]. Datasets in the non-relaxing condition were even more divers and ranged from waiting control conditions, to stressful exercises, to neutral tasks. The classification of relaxing vs. not could hence not capture all aspects within the data. Therefore, a number of more similar studies using the RSQ could shed more light on possible effects of a certain relaxation exercise or setting on the correlation between relaxation and sleepiness.

Conclusion

While relaxation and sleepiness are both associated with low arousal levels, the two constructs are two distinct dimensions. Using a broad variety of datasets, meta-analytical methods, and the Relaxation State Questionnaire [23], we could share some light on the relationship between the constructs that are based on two distinct arousal dimensions. While the data before a task or intervention suggests a consistency of the negative correlation between sleepiness and relaxation, the post-data showed a very mixed and inconsistent relationship between the two constructs. Thus, the intricated interplay between the two dimensions may be dependent on a variety of factors and may therefore change over short periods of time, even over the intercourse of an experiment. Even though more research is necessary to unravel the details of these changes, the present datasets and analyses may serve as a solid foundation for future explorations.

Supporting information

S1 Fig Funnel plot muscle scale pre-intervention.

Funnel Plot of the used datasets. Each point plotted represents the weighted z-transformed correlation of one dataset described in Table 2. The vertical dotted line represents the estimated correlation, as reported in Fig 1. The white triangle represents the region in which 95% of the data points should lie in absence of a selection bias.

(PDF)

S2 Fig Funnel plot general relaxation scale pre-intervention.

Funnel Plot of the used datasets. Each point plotted represents the weighted z-transformed correlation of one dataset described in Table 2. The vertical dotted line represents the estimated correlation, as reported in Fig 2. The white triangle represents the region in which 95% of the data points should lie in absence of a selection bias.

(PDF)

S3 Fig Funnel plot cardiovascular scale pre-intervention.

Funnel Plot of the used datasets. Each point plotted represents the weighted z-transformed correlation of one dataset described in Table 2. The vertical dotted line represents the estimated correlation, as reported in Fig 3. The white triangle represents the region in which 95% of the data points should lie in absence of a selection bias.

(PDF)

S4 Fig Funnel plot muscle scale post-intervention.

Funnel Plot of the used datasets. Each point plotted represents the weighted z-transformed correlation of one dataset described in Table 2. The vertical dotted line represents the estimated correlation, as reported in Fig 4. The white triangle represents the region in which 95% of the data points should lie in absence of a selection bias.

(PDF)

S5 Fig Funnel plot general relaxation scale post-intervention.

Funnel Plot of the used datasets. Each point plotted represents the weighted z-transformed correlation of one dataset described in Table 2. The vertical dotted line represents the estimated correlation, as reported in Fig 5. The white triangle represents the region in which 95% of the data points should lie in absence of a selection bias.

(PDF)

S6 Fig Funnel plot cardiovascular scale post-intervention.

Funnel Plot of the used datasets. Each point plotted represents the weighted z-transformed correlation of one dataset described in Table 2. The vertical dotted line represents the estimated correlation, as reported in Fig 6. The white triangle represents the region in which 95% of the data points should lie in absence of a selection bias.

(PDF)

S1 Table Mean correlations for each scale depending on intervention type.

n = number of studies in this category; p values in bold indicate significance.

(PDF)

S2 Table Mean correlations for each scale depending on the design.

n = number of studies in this category; p values in bold indicate significance.

(PDF)

10.1371/journal.pone.0310034.r001
Decision Letter 0
Steinborn Michael B. Section Editor
© 2024 Michael B. Steinborn
2024
Michael B. Steinborn
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
15 Jul 2024

PONE-D-24-21576Feeling tired vs. feeling relaxed: Two faces of low physical arousalPLOS ONE

Dear Dr. Steghaus,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Editor comments. I was able to appoint only one reviewer to comment on your manuscript. I asked a large number of potential referees, but so far without success. It is currently very difficult to find reviewers, and this seems to be a widespread issue. Therefore, I decided to step in this time and act as the second reviewer. We need a second referee for the final phase of the manuscript, but for now, my input should suffice to maintain the workflow and to give you some feedback for your work. Both R1 and my own reading suggest that this manuscript has the potential for significant impact. The manuscript has many strengths, particularly in the theoretical domain. It addresses a very general and relevant problem for nearly every field, differentiating theoretically between similar-sounding concepts. R1 has some issues that should be addressed in a proper revision. I suggest providing a response letter in the revision that addresses all issues in a point-by-point reply.

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Additional Editor Comments:

R1 raises valid and constructive points aimed at enhancing clarity, coherence, and significance of your manuscript. R1 raises one point that concerns the motivation of the study and how it is placed within existing knowledge. R1 argues that the study confirms known relationships but the increment offered by the study should be justified more. Additionally, R1 suggests integrating the research question more thoroughly throughout the manuscript and discussing the implications of the findings more theoretically, specifically regarding the concepts of feeling tired versus feeling relaxed. This means that revising the manuscript should involve presenting a stronger theoretical foundation in the introduction that naturally converges to the research question, and in the discussion, elaborating on how the present findings of multiple studies change the theoretical landscape. I agree with this point, as it would indeed enhance the impact of the present study. In the following, I provide comments regarding various issues I find relevant for the revision. Additionally, I will address and discuss comments from R1 to clarify their meaning and how to handle them. I want to emphasise that my comments are intended to improve the manuscript, not to criticise your work. I do not expect you to agree with all of my points; in other words, I do not claim that my opinion is always correct.

(-1-) conformation versus novelty

R1 raises the issue of whether the results confirm previous knowledge or provide novel findings capable of reshaping the theoretical landscape in the field. They argue that the present meta-analysis confirms a negative correlation between relaxation and sleepiness, but it is unclear what new insights this study adds to existing research. R1 suggests the manuscript should explain the importance of understanding the relationship between relaxation and sleepiness and why a meta-analysis is indicated. While I completely agree with these points, I want to note that systematically verifying findings for consistency is per se important and valuable in empirical research fields. This means that "novelty" should not be misconstrued as "discovery", especially in a field of behavioural research that is prone to false-positive results. Therefore, in my view, a lack of novelty is not an issue simply because a correlation has been reported previously. It is crucial to recognise that many findings in the correlational domain of states are often inconsistent. Demonstrating consistency, particularly in relation to specific variables or experimental contexts, is in my view, both significant and valuable.

(-2-) introduction

R1 finds that the research question about the relationship between sleepiness and relaxation is not well-integrated into the manuscript at present, and I completely agree with this point. I have some comments on how to handle this issue in a revision.

(--) Energetic Arousal (EA) vs. Tense Arousal (TA)

Thayer (1990) conceptualised energetic arousal (EA) and tense arousal (TA) as independent yet interactive components of reportable mental states in everyday situations. EA relates to feelings of vigour and vitality, while TA pertains to feelings of tension and nervousness, and moreover, these dimensions can combine in various ways to yield different mixtures of mental states. For example, high Energetic Arousal (EA+) and High Tense Arousal (TA+) is a state that corresponds to something we would call "challenge", this combination is often experienced in situations requiring high effort and involvement, such as competitive sports or high-stakes tasks, with the individual feeling energetic and engaged but also experiences a degree of stress or pressure. On the other hand, EA+ and TA- refers to a state that corresponds to excitement or pleasure, where individual feel lively and positive but in a relaxed way without the accompanying stress or extreme urgency to engage in an activity. This is typically observed in enjoyable activities where one is fully engaged but relaxed, such as playing a favourite sport for fun or engaging in a stimulating hobby. Further, EA- and TA+ is a state that corresponds to anxious agitation, this combination is marked by feelings of tension and worry without the counterbalance of energy. It is commonly experienced in situations perceived as threatening or overwhelming. Finally, EA- and TA- is a state that corresonds to apathy, which is a a state of low energy and low tension at the same time, often characterised by a lack of motivation and interest.

(--) Motivating the present study

While state questionnaires are often used in experimental settings, the concepts underlying these states are often not explicitly distinguished, as they are merely applied (i.e., this is partly because most researchers do not extensively investigate fields they consider peripheral to their own). However, this leads to the common issue of conflating similar-sounding constructs that have entirely different meanings (e.g., being tired vs. being relaxed), while treating seemingly dissimilar constructs (e.g., satiation vs. ego-depletion) as distinct (see Schumann et al., 2022, doi:10.3389/fpsyg.2022.867978, see Table 2). Researchers are indeed capable of distinguishing these if they engage with the specialised literature, but they often do not, as we have cognitive biases and limited resources. This exactly is the reason why specific knowledge from expert domains does typically not adequately permeate into other areas. Therefore, what is well-known and unremarkable to specialists can be a surprising and valuable insight to other researchers. It is therefore crucial to differentiate between what is "already known" by highly-specialised experts and what is common knowledge or "common sense" (what everyone knows) by everyone in a community of researchers covering various fields.

(--) precise research question

The pertinent question for virtually all fields of experimental psychological research (be it social, cognitive, or clinical) is to know how these state combinations affect performance in concrete settings, beyond everyday situations. To know means to understand systematically and coherently, not merely to be aware of one or some previous studies that have shown something (e.g., a correlational relationship) in a specific context using a specific sample of individuals. In experimental psychology, we do not speak of knowledge when referring to a single case but only when understanding a general principle. Therefore, it is crucial to examine how pre-test mental states relate to performance levels, how these states change over the course of a performance situation, and how they correlate with each other (e.g., before and after testing). I concur with the authors that it is vital to provide consistency and context variations across a variety of experiments. It is fundamentally different to play Tetris, to perform a simple-RT task, or engage in a low-event-rate vigilance task where nothing happens for extended periods. Moreover, it matters whether the performance situation is structured or motivated by goal settings, e.g., if mini-breaks are given or if mind-wandering, which equates to unregistered breaks, is possible (see: doi:10.3389/fpsyg.2022.867978; doi:10.3758/s13414-023-02803-4). Therefore, understanding these relationships is essential for elucidating the impact of mental states on performance outcomes. Conducting a systematic meta-analysis is an ideal approach to investigate this systematically.

(-3-) Pre- and Post-Intervention Data:

R1 notes that the value of using both pre- and post-intervention data is not well-articulated, which I agree and thereby would like to comment further on this aspect:

Considering pre-test to post-test intervals when conducting experiments is crucial for several reasons. Firstly, measuring states explicitly before and after the demand allows researchers to determine how mental states predict upcoming performance requirements. Secondly, it is essential to understand whether and how self-reported states change during performance demands, as this provides an indication of the costs or benefits these demands may generate on feelings. Thirdly, it provides insights into the contextual dynamics of the relationships between state dimensions, specifically how pre-test relationships might change correlatively over the course of an experiment. This is important because researchers often assume that traits are static and unchangeable, and not seldom, they implicitly extend this assumption to states, despite their inherent variability. To name one example of why this is absolutely crucial, Kärtner et al. (2021, doi:10.1038/s41598-021-81446-7) have shown that even stable traits are sensitive to change in some contexts. This suggests that not only states but even traits assumed to be stable by definition should be measured multiple times, or at least should be checked for variability in specific contexts. If even traits are somewhat variable and not static under some circumstances, this would clearly indicate that multiple measurements are, if not necessary, worth considering. This demonstrates that even firmly held beliefs can manifest empirically in completely different ways.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Using data from 11 experiments that used the Relaxation State Questionnaire (RSQ), the authors conducted a meta-analysis to investigate the relationship between relaxation and sleepiness. The authors found that the three aspects of relaxation as measured by the RSQ (cardiovascular, muscle, or general relaxation) were negatively correlated with the sleepiness factor of the RSQ when measured before any relaxation interventions. After relaxation interventions, however, the negative correlations between the three relaxation scales and the sleepiness scale were not only weaker, but also only the correlation with the muscle scale was significant. Further analyses which explored possible moderators did not report any significant effects.

This meta-analysis provides further evidence of the distinction between the low arousal states of relaxation and sleepiness, and while limited by its use of only the RSQ, uses a large, diverse dataset, and all the authors’ data and analytical scripts are openly available. However, my main point of revision is that, despite the relationship between the two states being described as “complex”, it is unclear what additional information has been learned from this meta-analysis beyond confirming the negative correlations observed in the original RSQ paper. Indeed, the way the manuscript is currently set up, it comes across that the negative relationship between relaxation and sleepiness has already been established, and why that relationship matters and why a meta-analysis is needed (for researchers, practitioners, or other audiences) is not strongly explained. Additionally, it is not clear to me what value has been added by using both the pre- and post-intervention data. The authors provide an intriguing research question at the end of the introduction (p. 10): “here we ask, how this intriguing negative relationship between sleepiness and relaxation behaves before and after interventions (that elicit a change in the subjective relaxed state) for each of the subscales of relaxation.” Yet this research question is absent from the abstract and conclusion, and when it is discussed, the authors focus on explaining the findings away rather than discussing why these changes, if real, matter and what they could tell us about the relationship between relaxation and sleepiness. In sum, I do not have strong objections to this manuscript's publication, but I believe this manuscript’s value to the academic literature would be greatly strengthened by the authors reshaping the manuscript to emphasize the importance of their findings.

Minor points of revision:

• Is Table 2 (p. 10, paragraph 2 under “Set of Studies”) the correct table? Table 2 appears to be one of the tables discussed in the “Further analyses” (which are otherwise in the Supporting Information); however, in the text Table 2 is described as information on the post-measurement samples, which seems to be a reference to Table 1.

• Although it can be figured out from the results, it would be helpful to explicitly note in either the “Set of Studies” (p. 10) or the “Data Analysis” section (p. 14) that the correlations are against the sleepiness scale of the RSQ. At the moment, it only says in the “Set of Studies” section that it will be for “each of the three relaxation scales of the RSQ.”

• The section for “Further analyses” (p. 16) discusses the lack of significant differences but does not discuss the results from the Bayes analyses, despite dedicating two sentences to explaining how to interpret Bayes Factors. A simple sentence on the weak BFs found would be sufficient to help readers who do not look at the Supporting Information.

• It would be helpful for readers to have titles for the tables and captions for the figures included in the Supporting Information. Apologies if these already exist; they aren’t present in the reviewer version.

**********

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Reviewer #1: No

**********

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10.1371/journal.pone.0310034.r002
Author response to Decision Letter 0
Submission Version1
21 Aug 2024

Review Comments to the Author (Reviewer #1):

Using data from 11 experiments that used the Relaxation State Questionnaire (RSQ), the authors conducted a meta-analysis to investigate the relationship between relaxation and sleepiness. The authors found that the three aspects of relaxation as measured by the RSQ (cardiovascular, muscle, or general relaxation) were negatively correlated with the sleepiness factor of the RSQ when measured before any relaxation interventions. After relaxation interventions, however, the negative correlations between the three relaxation scales and the sleepiness scale were not only weaker, but also only the correlation with the muscle scale was significant. Further analyses which explored possible moderators did not report any significant effects.

We would like to thank the reviewer for their helpful and constructive comments. Please find our responses to the comments below. We also uploaded a file with all the comments and our responses, where the original comments are repeated (in black), and our responses are written in italics and blue font.

This meta-analysis provides further evidence of the distinction between the low arousal states of relaxation and sleepiness, and while limited by its use of only the RSQ, uses a large, diverse dataset, and all the authors’ data and analytical scripts are openly available. However, my main point of revision is that, despite the relationship between the two states being described as “complex”, it is unclear what additional information has been learned from this meta-analysis beyond confirming the negative correlations observed in the original RSQ paper. Indeed, the way the manuscript is currently set up, it comes across that the negative relationship between relaxation and sleepiness has already been established, and why that relationship matters and why a meta-analysis is needed (for researchers, practitioners, or other audiences) is not strongly explained.

Thank you for your valuable comment. To shed more light on the possible dynamics and interaction between sleepiness and relaxation, added the following information to our introduction (page 3, lines 23 ff.):

Furthermore, the two dimensions can interact in different ways to produce various combinations of mental states [19]. EA and TA may be categorized into high (+) and low (-) states, allowing four different combinations of EA and TA. Table 1 shows a fourfold table with all possible combinations, giving everyday situations where they may occur.

Table 1. Fourfold table With Arousal Combinations

TA+ TA-

EA+ Challenge or high-stakes scenario

Individuals are very engaged, involved and energetic, but also under a certain degree of pressure, urgency or stress

Examples: Public speaking, important exam, competitive sports

Enjoyment or pleasure

Individuals are energetic and positive, but relaxed and calm at the same time

Examples: Non-competitive sports, playing games, pursuing a hobby

EA- Anxiousness

Individuals are tense, nervous and/ or worrying, but at the same time without energy

Examples: Threatening or overwhelming situations Passivity

Individuals are experiencing a lack of energy and enthusiasm while also experience no tension

Examples: Lack of motivation, apathy, indifference

TA+ = high tense arousal. TA- = low tense arousal. EA+ = high energetic arousal. EA- = low energetic arousal.

We also added a new section to the introduction (“Research Questions and Objectives”) to clarify our motivation for this study (p. 4, lines 31 ff.):

“Research Questions and Objectives

Further understanding the complex relationship between sleepiness and the facets of relaxation can impact both the theoretical understanding of the constructs and have a variety of practical implications, e.g., concerning stress management or well-being. While state questionnaires (and other measures) are frequently used in research, especially in experimental settings, the underlying concepts are often not clearly distinguished. This may lead to confusion between similar-sounding terms with different meanings (like being tired vs. being relaxed, for another example see [26]). In therapeutic settings for example, a gain in relaxation after an intervention would ideally be accompanied by a decrease in sleepiness during the day to allow patients to feel both calm, but still present, attentive, and observant (corresponding to Enjoyment from Table 1). Thus, it is vital to be able to understand how these two states combine, interact, and affect e.g., performance, well-being, and other outcomes in different settings. Especially since overarching states (such as relaxation and sleepiness) may very well be context specific as it was already demonstrated with other constructs [26,27]. To explore this, the present study analyses a set of 11 independent studies that all used the RSQ in various settings. Meta-analytic methods will then be able to reveal overarching trends for the relationship between sleepiness and relaxation over all studies.”

Additionally, it is not clear to me what value has been added by using both the pre- and post-intervention data. The authors provide an intriguing research question at the end of the introduction (p. 10): “here we ask, how this intriguing negative relationship between sleepiness and relaxation behaves before and after interventions (that elicit a change in the subjective relaxed state) for each of the subscales of relaxation.” Yet this research question is absent from the abstract and conclusion, and when it is discussed, the authors focus on explaining the findings away rather than discussing why these changes, if real, matter and what they could tell us about the relationship between relaxation and sleepiness.

Thank you again, for your very helpful comment. We now added further information on the pre-post distinction in the introduction by adding a new section to our “Research Question and Objectives”:

“Pre-Post-Data

The RSQ not only allows an efficient measurement of relaxation and sleepiness, but due to its briefness and its state-conception of the constructs, it gives the opportunity for measuring the changes in the states and their interaction over time, e.g., over the course of an experiment. Measuring states before and after an intervention is useful and insightful for several purposes: Measuring a state before a task or intervention allows researchers not only to get a baseline measurement of the participants mental state, but also to predict outcomes based on these measurements. Accordingly, post-measurements allow conclusions to be drawn e.g., about the effect or effectiveness of an intervention. Looking closer at the dynamics and interactions of mental states before and after interventions may give insights into specific changes of feelings or perceptions of individuals due to a task or intervention. This provides researchers with the opportunity to closer examine the contextual dynamics e.g., of the relationship between sleepiness and relaxation overall.

It could already be shown that even traits (which per definition are supposed to be stable) may be sensitive to change [e.g., 28,29]. The closer examination of states (which per definition are sensitive to change) and their dynamics and relationships may thus be insightful and enlightening. Therefore, here we also ask, how the intriguing negative relationship between sleepiness and relaxation behaves before and after interventions (that elicit a change in the subjective relaxed state) for each of the subscales of relaxation.”

(p. 5, lines 5 ff.)

Additionally, we added some sentences for further clarification in the discussion: Firstly, in the summary at the beginning of the discussion (p. 12, line 10):

“[…] this paper makes use of a broad variety of datasets to further investigate the relationship between relaxation and sleepiness and their dynamics before and after interventions.”

Secondly, as a short summary regarding the pre-data findings (p. 12, lines 27 ff.):

“Our findings suggest that this relationship is consistent over different settings and environments.”

And thirdly, as the reviewer suggested, we addressed the topic in our conclusion (p. 14, lines 11 ff.):

“While the data before a task or intervention suggests a consistency of the negative correlation between sleepiness and relaxation, the post-data showed a very mixed and inconsistent relationship between the two constructs. Thus, the intricated interplay between the two dimensions may be dependent on a variety of factors […]”

We also re-wrote parts of our abstract (p. 2, lines 5 ff. and 15 ff.):

“Relaxation and sleepiness are both assumed to be states of low physiological arousal and negatively correlated. However, it is still unclear how consistent this negative relationship is across different settings and whether it changes before and after an intervention.”

[…]

“For the post-intervention relationship, this negative correlation could not be consistently found. This indicates that there are aspects of the experimental setting or intervention that introduce changes in the dynamics of the relationship of the two constructs.”

In sum, I do not have strong objections to this manuscript's publication, but I believe this manuscript’s value to the academic literature would be greatly strengthened by the authors reshaping the manuscript to emphasize the importance of their findings.

Thank you again for your helpful and constructive feedback!

Minor points of revision:

• Is Table 2 (p. 10, paragraph 2 under “Set of Studies”) the correct table? Table 2 appears to be one of the tables discussed in the “Further analyses” (which are otherwise in the Supporting Information); however, in the text Table 2 is described as information on the post-measurement samples, which seems to be a reference to Table 1.

Thank you for pointing that out, indeed we meant to reference the other table at that point and therefore corrected it.

• Although it can be figured out from the results, it would be helpful to explicitly note in either the “Set of Studies” (p. 10) or the “Data Analysis” section (p. 14) that the correlations are against the sleepiness scale of the RSQ. At the moment, it only says in the “Set of Studies” section that it will be for “each of the three relaxation scales of the RSQ.”

Again, thank you for your feedback. We clarified this as you suggested in the “Data Analysis” section: “Each correlation was against the Sleepiness Scale of the RSQ.”

(p. 8 line 6).

• The section for “Further analyses” (p. 16) discusses the lack of significant differences but does not discuss the results from the Bayes analyses, despite dedicating two sentences to explaining how to interpret Bayes Factors. A simple sentence on the weak BFs found would be sufficient to help readers who do not look at the Supporting Information.

Thank you for this comment. We followed your advice and added the following sentences to the “Further Analyses” section: “The Bayes Factor mostly supports these findings, indicating moderate evidence only for the significant comparison. Furthermore, two more BF were just above 1, indicating weak evidence for the alternative hypothesis.” (p.10, lines 17 ff.).

• It would be helpful for readers to have titles for the tables and captions for the figures included in the Supporting Information. Apologies if these already exist; they aren’t present in the reviewer version.

We apologize for the inconvenience, however, according to the Journal’s guidelines (“Do not include captions as part of the figure files”), the captions should only be placed in the text.

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0310034.r003
Decision Letter 1
Steinborn Michael B. Section Editor
© 2024 Michael B. Steinborn
2024
Michael B. Steinborn
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
23 Aug 2024

Feeling tired versus feeling relaxed: Two faces of low physiological arousal

PONE-D-24-21576R1

Dear Dr. Steghaus,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Final editor comments. The authors have convincingly addressed all points and reworked the manuscript, which is now in excellent shape. I am thoroughly impressed by the attention to detail, as well as the instructive nature of the paper. This dual function to write the manuscript so that it is both a concise tutorial on measurement of state and feelings and an empirical study presenting findings based on data is highly effective. I have reread the manuscript and reviewed the commens of R1, finding everything perfectly addressed. Therefore, after due consideration, I have decided that the manuscript, in its present form, can be accepted.

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Kind regards,

Michael B. Steinborn, PhD

Section Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

10.1371/journal.pone.0310034.r004
Acceptance letter
Steinborn Michael B. Section Editor
© 2024 Michael B. Steinborn
2024
Michael B. Steinborn
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
29 Aug 2024

PONE-D-24-21576R1

PLOS ONE

Dear Dr. Steghaus,

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==== Refs
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